# Estimate models
m1 <- mdlr.conflict(dv="I(100*cw_onset_combined)",
                    x= c("lost_ga_pop"),
                    cntr=c("bal_pop","tek_link","pop_maj_ag_23"),
                    fe= c("year","time_since_cw_combined"),
                    model="ols",
                    dat=segments.df
)
summary(m1,cluster=c("id_cap_rule_corr","ag_id"))


m2 <- mdlr.conflict(dv="I(100*cw_onset_combined)",
                    x= c("lost_ga_pop"),
                    cntr=c("bal_pop","tek_link","pop_maj_ag_23"),
                    fe= c("year","id_cap_rule_corr","time_since_cw_combined"),
                    model="ols",
                    dat=segments.df
)
summary(m2,cluster=c("id_cap_rule_corr","ag_id"))


m3 <- mdlr.conflict(dv="I(100*cw_onset_combined)",
                    x= c("lost_ga_pop"),
                    cntr=c("bal_pop","tek_link","pop_maj_ag_23",
                           "log(popc_cntr)","log(popc_ag_corr)","log(popc_corr)",
                           "pop_frac_ag_alt","pop_frac_cntr_alt",
                           "log(1+dist_capital_km)","war_hist_cw_yrs"),
                    fe= c("year","id_cap_rule_corr","time_since_cw_combined","time_in_current_border"),
                    model="ols",
                    dat=segments.df)
summary(m3,cluster=c("id_cap_rule_corr","ag_id"))


m4 <- mdlr.conflict(dv="I(100*cw_onset_combined)",
                    x = c("lost_capital_only_pop","lost_unity_only_pop","lost_both_pop"),
                    cntr=c("bal_pop","tek_link","pop_maj_ag_23",
                           "log(popc_cntr)","log(popc_ag_corr)","log(popc_corr)",
                           "pop_frac_ag_alt","pop_frac_cntr_alt",
                           "log(1+dist_capital_km)","war_hist_cw_yrs"),
                    fe= c("year","id_cap_rule_corr","time_since_cw_combined","time_in_current_border"),
                    model="ols",
                    dat=segments.df)
summary(m4,cluster=c("id_cap_rule_corr","ag_id"))


m5 <- mdlr.conflict(dv="I(100*cw_onset_combined)",
                    x= c("lost_ga_pop_16",
                         "lost_ga_pop_pre16"),
                    cntr=c("bal_pop","tek_link","pop_maj_ag_23",
                           "log(popc_cntr)","log(popc_ag_corr)","log(popc_corr)",
                           "pop_frac_ag_alt","pop_frac_cntr_alt",
                           "log(1+dist_capital_km)","war_hist_cw_yrs"),
                    fe= c("year","id_cap_rule_corr","time_since_cw_combined","time_in_current_border"),
                    model="ols",
                    dat=segments.df)
summary(m5,cluster=c("id_cap_rule_corr","ag_id"))


### make table
m.list <- summary(.l(list(m1,m2,m3,m4,m5)), 
                  cluster=c("id_cap_rule_corr","ag_id"))


var.labs <- c("lost_ga_pop" = "Lost home rule or lost unity",
              "lost_ga_pop_16" = "Lost home rule or lost unity (post-1816)",
              "lost_ga_pop_pre16" = "Lost home rule or lost unity (pre-1816)",
              "lost_capital_only_pop" = "Lost home rule only",
              "lost_unity_only_pop" = "Lost unity only",
              "lost_both_pop" = "Lost home rule & lost unity",
              "id_cap_rule_corr" = "State","ag_id" = "Aggregate group",
              "year" = "Year", "time_since_cw_combined" = "Peace year",
              "time_in_current_border"="Border duration",
              "I(100*cw_onset_combined)"="Ethnic civil war onset $\\times 100$"
)



etable(m.list,tex=T, keep = c("lost","Lost"),
       dict = var.labs,
       title="Civil War Onset: Population-based variables",
       label="tab:cw_pop",
       style.tex = style.tex(main="base"), 
       file=file.path(tab.path,"table_A11.tex"),replace=T,
       extralines = list("-_Baseline controls"=rep("Yes",5),
                         "-_Extended controls"=rep(c("","Yes","Yes","Yes","Yes"),1)),
       signif.code = c("***"=0.001,"**"=0.01,"*"=0.05,"+"=0.1),
       fitstat = c("n"),
       notes = c("\\parbox[t]{\\width=\\textwidth}{\\textbf{Notes:} OLS estimates of Civil War Onsets. The unit of analysis is the ethnic segment year. 
       Baseline controls: segment population relative to state-leading group, transborder ethnic kin dummy, national unity dummy. 
       Extended controls: logged country and aggregate group population; ethnic fractionalization of country and aggregate group;
       logged distance to capital; war history (past years with ongoing civil war);
       time since last border change (FE). Standard errors clustered on country and aggregate ethnic group in parentheses.
       Significance codes: ***: 0.001, **: 0.01, *: 0.05, +: 0.1}"))
